A New Learning Paradigm for Stochastic Configuration Network: SCN+
This provides a new method for training SCN with privileged information, but it is incremental as it adapts an existing paradigm to a specific network type.
The paper tackles the problem of training stochastic configuration networks (SCN) by introducing SCN+, an incremental learning algorithm that incorporates the learning using privileged information (LUPI) paradigm, and experimental results show it performs favorably.
Learning using privileged information (LUPI) paradigm, which pioneered teacher-student interaction mechanism, makes the learning models use additional information in training stage. This paper is the first to propose an incremental learning algorithm with LUPI paradigm for stochastic configuration network (SCN), named SCN+. This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN. Moreover, the convergences have been studied in this paper. Finally, experimental results indicate that SCN+ indeed performs favorably.